Machine Learning Syllabus

 

🤖 Machine Learning (ML) Program Overview
Our structured ML learning paths are designed to take you from beginner to advanced, using hands-on projects, industry-relevant tools, and real-world problem-solving.

🔹ML – Basic Level (4 Weeks)

🧠 Key Topics Covered:

  • What is Machine Learning?

  • Real-world ML applications

  • Types of ML: Supervised vs Unsupervised

  • Introduction to popular ML algorithms

  • Handling missing data

  • Encoding categorical variables

  • Feature scaling (Normalization, Standardization)

  • Exploratory Data Analysis (EDA)

  • Linear Regression – Predict continuous values

  • K-Nearest Neighbors (KNN) – Simple classification

  • Decision Trees – Tree-based decision making

  • Accuracy, Precision, Recall, F1 Score

  • Confusion Matrix, Cross-validation

  • Mini Project:

    • 🏠 House Price Prediction

    • 📊 Customer Segmentation (KMeans)

🧰 Tools You'll Use

  • Python
  • NumPy, pandas
  • matplotlib, seaborn
  • scikit-learn

🔹 ML – Advanced Level (6 Weeks)

🧠 Key Topics Covered:

  • Introduction to ensembles

  • Random Forest – Bagging technique

  • Gradient Boosting – Boosted trees (XGBoost, LightGBM)

  • Grid Search CV & Randomized Search CV

  • Cross-validation strategies

  • Bias vs Variance trade-off

  • Using Pipeline in scikit-learn

  • Handling real-world workflows

  • Saving and loading models

  • Time series fundamentals

  • Stationarity, trends, seasonality

  • Forecasting with ARIMA, Prophet

  • Principal Component Analysis (PCA) for dimensionality reduction

  • Hierarchical Clustering

  • DBSCAN (Density-Based Clustering)

  • Basics of Flask and Stream lit

  • Deploying a model to a web app

  • Monitoring predictions

🧰 Tools You'll Use

  • Python
  • scikit-learn, XGBoost, LightGBM
  • Flask, Streamlit
  • pandas, NumPy, matplotlib, seaborn